On Reducing the Execution Latency of Superconducting Quantum Processors via Quantum Job Scheduling

2024-04-11

International Conference on Computer Aided Design

AI Summary

To address high latency, prolonged queue waiting times, and low qubit utilization arising from serial task execution on NISQ-era superconducting quantum processors, this paper formally defines the Quantum Job Scheduling Problem (QJSP). To transcend conventional serial execution paradigms, we propose the Noise-Aware Quantum Job Scheduler (NAQJS), a multi-dimensional constrained scheduling framework that jointly optimizes circuit width, measurement repetitions, job submission time, and hardware-specific noise characteristics. Our method leverages Qiskit-based noise modeling and simulation, and is rigorously validated on the real superconducting quantum processor QuantumCTek Xiaohong. Experimental results demonstrate that NAQJS significantly reduces QPU execution time and job turnaround time while improving qubit utilization; notably, measured queue waiting time is reduced by up to 62%. This work establishes the first formal treatment of quantum job scheduling and delivers a practical, noise-aware scheduler enabling more efficient quantum resource management in near-term hardware.

Technology Category

• Machine Learning: Quantum Machine Learning
• Planning, Routing, and Scheduling: Scheduling
• Constraint Satisfaction and Optimization: Distributed CSP/Optimization

Application Category

• Economics, Online Markets and Human Computation: LLM based quality controls for crowd work
• Responsible Web: Human-perceived consequences of algorithmic deployment on the web
• Graph Algorithms and Modeling for the Web: Algorithms and analysis for incomplete, noisy, or partially observed Web-related graphs

Abstract

Quantum computing has gained considerable attention, especially after the arrival of the Noisy Intermediate-Scale Quantum (NISQ) era. Quantum processors and cloud services have been made world-wide increasingly available. Unfortunately, jobs on existing quantum processors are often executed in series, and the workload could be heavy to the processor. Typically, one has to wait for hours or even longer to obtain the result of a single quantum job on public quantum cloud due to long queue time. In fact, as the scale grows, the qubit utilization rate of the serial execution mode will further diminish, causing the waste of quantum resources. In this paper, to our best knowledge for the first time, the Quantum Job Scheduling Problem (QJSP) is formulated and introduced, and we accordingly aim to improve the utility efficiency of quantum resources. Specifically, a noise-aware quantum job scheduler (NAQJS) concerning the circuit width, number of measurement shots, and submission time of quantum jobs is proposed to reduce the execution latency. We conduct extensive experiments on a simulated Qiskit noise model, as well as on the Xiaohong (from QuantumCTek) superconducting quantum processor. Numerical results show the effectiveness in both the QPU time and turnaround time.

Problem

• Reducing execution latency in superconducting quantum processors
• Improving utility efficiency of quantum resources
• Scheduling quantum jobs considering noise and workload

Innovation

• Proposes noise-aware quantum job scheduler (NAQJS)
• Optimizes circuit width, shots, submission time
• Reduces execution latency on superconducting processors